{"as_of":"2026-08-15T08:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b467079b4c9d03c97a3b652f676d09ec681b4fbff2c9bff4776d0fc204c942e","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T23:25:41.172564Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.04170/citation-record","integrity":"/paper/2502.04170/integrity","json":"/paper/2502.04170/citation-record.json","paper":"/paper/2502.04170"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.007310Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.007310Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:69204ff315c81965c729e20d8415d991cc9e3090ae91ff73603dbc275cb18f8c","observation_id":"c80a562e-ea58-4e35-a47a-5a264fb98d5e","resolution":{"observed_at":"2026-08-08T23:25:41.007310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.732535Z","title":"Algorithmic motion plan- ning,","venue":null,"work_id":"eaeee5cf-8c41-4039-bec7-b562c4995a61","year":2017},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.013416Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:089b45662e967cad6fe641d90af0a1ad4640662ec243497fa08a9f254cb8f159","observation_id":"1e888732-64e0-4eaa-a5a2-136161ad4722","resolution":{"observed_at":"2026-08-08T23:25:41.737282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.716899Z","title":"Latombe, Robot motion planning","venue":null,"work_id":"e34e82ea-f57c-4fd8-9f99-83415052e085","year":2012},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.018161Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:0dec0b5e2bef5ac1e24fe7e5f979cdb98e47f14b1c2976fa5271701345b6483f","observation_id":"90d57604-80ae-47a1-a549-3f6548bd4e8f","resolution":{"observed_at":"2026-08-08T23:25:41.722201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.701463Z","title":"Sampling-based algorithms for optimal motion planning,","venue":null,"work_id":"196320b2-b924-4aa9-9a2e-257b8c4e4b40","year":2011},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.022729Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:aed19ba81a744e9b6cec04c10530ab8d78f0983af935e8434e456f3ed6afb507","observation_id":"23ffa810-dba1-4ac5-84f1-5ba6c4a0bab7","resolution":{"observed_at":"2026-08-08T23:25:41.706503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.027417Z","title":"Sampling-based robot motion planning: A review,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.027417Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:8ce1f4d57847516fdf6e0d7dba25a67ef5d4119c0b3010ed37fc1b8e986611f3","observation_id":"7d7e3700-7177-42bc-964f-08ae2d988a61","resolution":{"observed_at":"2026-08-08T23:25:41.027417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.675446Z","title":"CHOMP: co- variant hamiltonian optimization for motion planning,","venue":null,"work_id":"c948af73-f952-4dba-8030-f5a75ea23bfb","year":2013},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.031841Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:71fc6fc33fffd42b0a9f58030989c97ce5db92c9f37838ab894b06cdee29c82b","observation_id":"757677a9-026a-42d8-888a-d8cc6db475bf","resolution":{"observed_at":"2026-08-08T23:25:41.680753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.660318Z","title":"Single- and dual-arm motion planning with heuristic search,","venue":null,"work_id":"3aed550f-440f-4355-b783-58dbaa1bba69","year":2014},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.036766Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:a31fd0efb96e55bd2c570ae20b7103a00994c8088b53e68429b4c8d1a2b7f8e0","observation_id":"2782fd92-0475-49bc-bff1-305f39faaaae","resolution":{"observed_at":"2026-08-08T23:25:41.665376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.645330Z","title":"Sampling-based robot motion planning,","venue":null,"work_id":"8a7c7eb2-7e90-4363-845f-ab29fb6101c0","year":2019},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.041104Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:a43e0d7288b19f5ef1ef5529f092ba753ded1772671d9a10b7632a41e88d6c8e","observation_id":"12ef295d-c5d0-4868-8f26-58340c4fe244","resolution":{"observed_at":"2026-08-08T23:25:41.650314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.629724Z","title":"Lazy collision checking in asymptotically-optimal motion planning,","venue":null,"work_id":"89a91c0b-c329-4ffd-995c-43ef6540ef37","year":2015},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.045456Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:2228b67f1d733947a4a4fb3b69788cb1b6b4b360b0b86b0b8d4aacdffb3851eb","observation_id":"1e3575ae-d7cb-4192-b4c4-963471e0cd90","resolution":{"observed_at":"2026-08-08T23:25:41.634741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.614421Z","title":"Proba- bilistic roadmaps for path planning in high-dimensional configuration spaces,","venue":null,"work_id":"2b11c7bd-1a01-4a15-beb3-86bed6fb6c1c","year":1996},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.049900Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:f757af2598cbfa9f5b437af8e7b2258a733061cee3467bdbddd76b4dbae12f31","observation_id":"e3772fae-3f7f-470f-bd77-1ab31e3cc812","resolution":{"observed_at":"2026-08-08T23:25:41.619546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.598991Z","title":"Randomized kinodynamic plan- ning,","venue":null,"work_id":"9799e469-35af-4d37-8e95-e857c8024cb7","year":2001},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.054290Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:e0730cadd37e3796488666bfc602b999539af7fdb32be1736483cd4131e7a9eb","observation_id":"fe15fd86-62c8-4d0b-aaf7-5c4664570ce8","resolution":{"observed_at":"2026-08-08T23:25:41.604147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.583425Z","title":"A unifying formalism for shortest path problems with expensive edge evaluations via lazy best-first search over paths with edge selectors,","venue":null,"work_id":"b45776aa-1aff-4a04-b05f-96c4df18075a","year":2016},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.058922Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:187c677ddfc885c2dea8db45fbcb2a2918498ffb680f84233554632fcee6a66e","observation_id":"2c579ed0-eefa-4fda-a506-d9d2a87d85d7","resolution":{"observed_at":"2026-08-08T23:25:41.588706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.567845Z","title":"Gen- eralized lazy search for robot motion planning: Interleaving search and edge evaluation via event-based toggles,","venue":null,"work_id":"78253b43-fd26-4751-b0e4-1f4ee3b2a7d6","year":2019},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.063390Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:c409c61cb65e1158335b386ba3e0815535fb71839068b2908a90b6cddb8ce616","observation_id":"36c0c24c-32b0-4261-a802-edde69bdf040","resolution":{"observed_at":"2026-08-08T23:25:41.573022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.551890Z","title":"Learning-based proxy collision detection for robot motion planning applications,","venue":null,"work_id":"fb178d64-114f-4f08-878a-59d1dd76ef07","year":2020},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.067742Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:e4c3f232367d337ef93fb7f226bc54831747b58b13f1f9f6589afbd60a9c16cd","observation_id":"dcd23bac-0938-4c56-b5f1-4018c5fa49de","resolution":{"observed_at":"2026-08-08T23:25:41.557050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.536344Z","title":"DiffCo: Autodifferentiable proxy collision detection with multiclass labels for safety-aware trajectory optimization,","venue":null,"work_id":"4f12d520-5562-4676-8611-7fba2cfaae56","year":2022},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.071991Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:aada1916aa542655c645d6c49fb874eb5fed5eb9e3530c25df3b2022b303ff86","observation_id":"af6305b1-168e-442a-8052-752d62b84ac4","resolution":{"observed_at":"2026-08-08T23:25:41.542125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.521015Z","title":"Collisiongp: Gaussian process-based collision checking for robot motion planning,","venue":null,"work_id":"4ec6e0d1-4bc2-4649-9e1e-55e616357576","year":2023},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.076178Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:5b5bd191399149d542e10907195edad98e66eca25955d5c66fab3dc5eb4a32bf","observation_id":"76ddb65c-dd32-4842-bc60-1c0328847ea4","resolution":{"observed_at":"2026-08-08T23:25:41.526166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.487233Z","title":"Neural collision clearance estimator for batched motion planning,","venue":null,"work_id":"87fcb99f-8093-48df-aa5f-d720e3417bfe","year":2020},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.084924Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:b46fdbf8bdfb91c8506d06541e5f1eef4441b13ee1c04737641689f529595c8a","observation_id":"3e963c96-d54f-45b3-b78c-f0bbc12f3cd5","resolution":{"observed_at":"2026-08-08T23:25:41.492410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.471419Z","title":"A survey of learning-based robot motion planning,","venue":null,"work_id":"165620a3-6e64-4385-b762-ed3022356d1f","year":2021},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.089114Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:80342d50b0467693fe3f1b9162bfe8b59ad83d0babd3efa0137430b4474ec40f","observation_id":"111d605c-26c3-41cf-b753-005d5d3edb33","resolution":{"observed_at":"2026-08-08T23:25:41.476788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.456053Z","title":"A survey on the integration of machine learning with sampling-based motion planning,","venue":null,"work_id":"cba71b4d-7a26-4373-80b0-7397b2204027","year":2022},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.093296Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:1b4be432afa7cbd354a5927c1051e3d70b33f8980963670ef3703b0056d151e2","observation_id":"b71f5678-6a66-49ef-b15b-41d9f6c52a3d","resolution":{"observed_at":"2026-08-08T23:25:41.461330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.440509Z","title":"Shalev-Shwartz and S","venue":null,"work_id":"63cd1796-a110-4fb0-8219-65aa70d47cb5","year":2014},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.098263Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:cf367d710f18c19f2da626fc10851aa3868f01cda1a563f6e9e2d44c955147e0","observation_id":"bebfa387-1140-4144-9f3e-6bf0746109fd","resolution":{"observed_at":"2026-08-08T23:25:41.445493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.425592Z","title":"Cristianini, An Introduction to Support Vector Machines and other kernel-based learning methods","venue":null,"work_id":"cde4e623-d831-4c74-b826-df8b05b7f3a8","year":2000},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.102671Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:983b7dbe16d77a493da8a7bad0a56733117319520752957a8ecc0856840b3978","observation_id":"a610128d-81e6-4ed2-8a91-c1c1531fe9d5","resolution":{"observed_at":"2026-08-08T23:25:41.430052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.107123Z","title":"Active learning literature survey,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.107123Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:d28fc2b9f2b91b76caa1d2f0a19bc718ad461646d34e49f7c0db3363a3888192","observation_id":"9ff309ed-867c-4683-96af-ffcb130a0327","resolution":{"observed_at":"2026-08-08T23:25:41.107123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.503741Z","title":"Forward kinematics kernel for improved proxy collision checking,","venue":null,"work_id":"dff9e6c4-adc9-4bf1-bde2-bf61f5c65f09","year":2020},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.111375Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:d73d126c5ec39303e558f03ec1c8ed9c612194a5b67cfbded65e830d783b88c0","observation_id":"09298ed7-46c4-4831-9adc-934b3efd3f28","resolution":{"observed_at":"2026-08-08T23:25:41.509539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.400048Z","title":"Reducing collision checking for sampling-based motion planning using graph neural networks,","venue":null,"work_id":"c92d5e12-f7a1-49e6-b938-353785c96df7","year":2021},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.115626Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:56880df4e3ef7541e9d52085f2367b106d07e089ef2eda29581059d422d7afb0","observation_id":"fbe6110a-f977-4ce2-9b24-fbc1531c3b86","resolution":{"observed_at":"2026-08-08T23:25:41.406634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.119973Z","title":"Graph neural networks: A review of methods and applications,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.119973Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:e9881410b005bc05161931b8145001bfaa822ad9160ffe524bcb5ea1a39f5034","observation_id":"fbb9033d-aefa-49f1-b8dc-a6442a3dee2a","resolution":{"observed_at":"2026-08-08T23:25:41.119973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.375381Z","title":"Learning-based motion planning in dynamic environments using GNNs and temporal encoding,","venue":null,"work_id":"ba35399c-476b-4b0a-bfd8-c9df18a2dafd","year":2022},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.124180Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:9a02e47036a2e77ba2625a0ea6e9fbd6dd71b230ffa423c8a9289fe0292193f1","observation_id":"6a0cec71-50f8-4ec0-9ba9-03b625aa7a28","resolution":{"observed_at":"2026-08-08T23:25:41.380077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.361129Z","title":"Configuration space distance fields for manipulation planning,","venue":null,"work_id":"fe15c3f6-b6e5-4ddb-bdba-1324088d6d95","year":2024},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.128624Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:1475cdfe5a23adfa9c1894b5f7368b1c1ed4087c36a989cef0bc2d0d9682fb3b","observation_id":"b2a80b2b-7e2b-4c45-88bc-f934cb2685cf","resolution":{"observed_at":"2026-08-08T23:25:41.365673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.346242Z","title":"Neural joint space implicit signed distance functions for reactive robot manipulator control,","venue":null,"work_id":"b140b218-63d7-4e8a-bdd5-3a0222e64e77","year":2022},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.133008Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:21c0a9b3e91c21aa82a5e47f87a5a713efcde0179e878315e512caa287b623ce","observation_id":"35a1d5b0-0e21-4fb0-8680-8a2392eb8270","resolution":{"observed_at":"2026-08-08T23:25:41.350970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.332293Z","title":null,"venue":null,"work_id":"56647a70-1eac-482d-bbc9-f3ef64c5f8cb","year":2015},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.137414Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:3c1ccd8b7af3d1ebc31b626adf3705a87e434faad52b3e3256b81a7a034c98bb","observation_id":"3235a500-7a1f-4348-83fd-41c8d2c444c1","resolution":{"observed_at":"2026-08-08T23:25:41.336714Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.317366Z","title":"Sample complexity of prob- abilistic roadmaps via ϵ-nets,","venue":null,"work_id":"7ad6ffa2-c986-4d9a-8ec2-edb40b43934e","year":2020},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.141784Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:6f54d9ff3668cd70a37085be633922d4cc5216b0da07fcac16902d443c41aa66","observation_id":"0b78b61a-cf16-4efa-83ea-b026d01ee8df","resolution":{"observed_at":"2026-08-08T23:25:41.322187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.301147Z","title":"Near-optimal multi-robot motion planning with finite sampling,","venue":null,"work_id":"7f2d9b47-bd60-47a2-ad67-b212b8bf3afe","year":2023},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.146078Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:4d26af758fcd77503f2603d3869387fdeb490aea7ff260c3f932b9eab559fd37","observation_id":"89180fd5-0fc3-44be-b0e4-9481c6829196","resolution":{"observed_at":"2026-08-08T23:25:41.306558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17394","last_updated":"2024-12-04T19:02:59Z","snapshot_observed_at":"2026-08-12T23:15:31.171236Z","submitted_at":"2024-07-24T16:17:03Z","title":"Towards Practical Finite Sample Bounds for Motion Planning in TAMP","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17394","snapshot_observed_at":"2026-08-08T23:25:41.150410Z","title":"Towards practical finite sample bounds for motion planning in TAMP,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.150410Z"},"links":{"cited_paper":"/paper/2407.17394","citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:7b18d26b946cf4b9afa885e0124c4f8436abe1810dd06510b683c9f5ec29290d","observation_id":"ed6cef13-a106-49cd-8818-c71d90be28d8","resolution":{"observed_at":"2026-08-08T23:25:41.150410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.284414Z","title":"Integrated task and motion plan- ning,","venue":null,"work_id":"d5d0e013-3b13-45bd-bc53-bf48fdec4ade","year":2021},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.155080Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:8fa50d1fc13dde770c4f2bbc74e42f5fc2d1534fb3a55d6ad84832395b3a2acd","observation_id":"a8c97a22-00e4-4e61-88b4-4dc5ebf6f83e","resolution":{"observed_at":"2026-08-08T23:25:41.289630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.269386Z","title":"Wackerly, W","venue":null,"work_id":"c8986e79-cec0-4f8c-8672-3bfa07a4a01e","year":2008},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.159569Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:55f8562fbea4dd6376ee2ed36a670b848c756da80f23360799bef4bf834c23bf","observation_id":"42b220e8-d688-4bdd-8544-724b5f2d1c26","resolution":{"observed_at":"2026-08-08T23:25:41.274089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.253612Z","title":"Approximate is better than “exact","venue":null,"work_id":"f90bd8de-6821-4800-a238-4864c03ba761","year":1998},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.163844Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:653ad5d63a77b94c9491905a72e8562bdad94c044bb3680bd49b48724c275b5f","observation_id":"696a3e6c-fc1d-4aeb-8ede-6172b92c5d34","resolution":{"observed_at":"2026-08-08T23:25:41.258517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.238405Z","title":"Confidence intervals for a binomial proportion and asymptotic expansions,","venue":null,"work_id":"4f248739-1bb8-4fba-a096-fa67987254eb","year":2002},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.168295Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:f0806c9d5b186fc8256df8f2741828104d6eec6eb93754b4344049132d4ee168","observation_id":"7eb491e0-82e1-4373-9f1c-adf270c3d202","resolution":{"observed_at":"2026-08-08T23:25:41.243141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:25:41.219651Z","title":"Interval estimation for a binomial proportion,","venue":null,"work_id":"1287e726-d720-4d0a-9666-4f8bfa8b0596","year":2001},"citing_paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T23:25:41.172564Z"},"links":{"citing_paper":"/paper/2502.04170"},"observation_digest":"sha256:bfea457de9846177baa9d824279364c2fd183c1214cf87f74fbfc584ae825f0f","observation_id":"8e669313-c1de-4ffb-a16a-8c5b7fb5deba","resolution":{"observed_at":"2026-08-08T23:25:41.226511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04170","last_updated":"2025-02-06T15:58:30Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-14T02:30:04.415576Z","submitted_at":"2025-02-06T15:58:30Z","title":"From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":37},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2502.04170."}